mlsanityData to 7 Oct 2026

The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research

arXiv 2305.02797 ↗First posted 4 May 2023cs.CLcited by 27

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Abstract (arXiv)

Recent advances in deep learning methods for natural language processing (NLP) have created new business opportunities and made NLP research critical for industry development. As one of the big players in the field of NLP, together with governments and universities, it is important to track the influence of industry on research. In this study, we seek to quantify and characterize industry presence in the NLP community over time. Using a corpus with comprehensive metadata of 78,187 NLP publications and 701 resumes of NLP publication authors, we explore the industry presence in the field since the early 90s. We find that industry presence among NLP authors has been steady before a steep increase over the past five years (180% growth from 2017 to 2022). A few companies account for most of the publications and provide funding to academic researchers through grants and internships. Our study shows that the presence and impact of the industry on natural language processing research are significant and fast-growing. This work calls for increased transparency of industry influence in the field.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 10 of its 57 references that are in the map. First posted on arXiv 4 May 2023.

Builds on

10 of 57 references.

References · 10

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2020The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Researchcited by 67
  4. 2020The Grey Hoodie Project: Big Tobacco, Big Tech, and the threat on academic integritycited by 38
  5. 2020Examining Citations of Natural Language Processing Literaturecited by 20
  6. 2020Gender Gap in Natural Language Processing Research: Disparities in Authorship and Citationscited by 17
  7. 2022D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Researchcited by 11
  8. 2015A Review of Theory and Practice in Scientometricscited by 6
  9. 2021DRIFT: A Toolkit for Diachronic Analysis of Scientific Literaturecited by 4
  10. 2022CS-Insights: A System for Analyzing Computer Science Researchcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 8; 2025: 4; 2026: 8.

27 papers.

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  1. 2023Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Modelscited by 186
  2. 2023Position: Key Claims in LLM Research Have a Long Tail of Footnotescited by 20
  3. 2023We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fieldscited by 20
  4. 2024Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challengescited by 18
  5. 2024Agreement-Based Cascading for Efficient Inferencecited by 16
  6. 2023Forgotten Knowledge: Examining the Citational Amnesia in NLPcited by 13
  7. 2023Regulation and NLP (RegNLP): Taming Large Language Modelscited by 5
  8. 2024Citation Amnesia: On The Recency Bias of NLP and Other Academic Fieldscited by 5
  9. 2023To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processingcited by 4
  10. 2024The complementary contributions of academia and industry to AI researchcited by 4
  11. 2024On Tables with Numbers, with Numberscited by 4
  12. 2024Building Better: Avoiding Pitfalls in Developing Language Resources when Data is Scarcecited by 4
15 more
  1. 2025Misalignment or misuse? The AGI alignment tradeoffcited by 4
  2. 2023Collaboration or Corporate Capture? Quantifying NLP's Reliance on Industry Artifacts and Contributionscited by 2
  3. 2024What We Talk About When We Talk About LMs: Implicit Paradigm Shifts and the Ship of Language Modelscited by 2
  4. 2025What Do Indonesians Really Need from Language Technology? A Nationwide Surveycited by 2
  5. 2026Reviewer Scores Are Not Comparable Across Research Areas in ML Peer Reviewcited by 2
  6. 2026Quantifying the Knowledge Proximity Between Academic and Industry Research: An Entity and Semantic Perspectivecited by 1
  7. 2026ClaimFlow: Tracing the Evolution of Scientific Claims in NLPcited by 1
  8. 2024Data Processing for the OpenGPT-X Model Familycited by 0
  9. 2025Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Biascited by 0
  10. 2025Big AI is accelerating the metacrisis: What can we do?cited by 0
  11. 2026Structural shifts in institutional participation and collaboration within the AI arXiv preprint research ecosystemcited by 0
  12. 2026Tracing the Evolution of Word Embedding Techniques in Natural Language Processingcited by 0
  13. 2026LLMs in the Real World: Evaluating "AI" in Emergency Contextscited by 0
  14. 2026The Future of NLP may not be at NLP Conferences: Scholarly Migration Patterns in Natural Language Processingcited by 0
  15. 2026Beyond Good Intentions: When Does the Framing of Multilingual and Low-Resource NLP Research Become a Caricature?cited by 0